The Effects of a Predictive HMI and Different Transition Frequencies on Acceptance, Workload, Usability, and Gaze Behavior during Urban Automated Driving

Hecht, Tobias; Kratzert, Stefan; Bengler, Klaus · 2020 · Crossref

DOI: 10.3390/info11020073

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Summary

This study investigates the impact of transition frequencies and predictive Human-Machine Interfaces (HMI) on user acceptance, workload, usability, trust, and gaze behavior during urban automated driving. As research shifts from critical take-over scenarios to predictable transitions in SAE Level 3 and 4 automation, understanding how frequent interruptions affect non-driving related activities (NDRA) is crucial. The authors aimed to determine if a predictive HMI, which informs users of upcoming system limits, could mitigate negative effects caused by varying frequencies of requests to intervene (RtIs). The researchers conducted a driving simulator study with 33 participants using a dynamic seat box and head-mounted eye-tracking. The experiment employed a mixed design with a between-subject factor for HMI concept (baseline vs. predictive) and a within-subject factor for transition frequency (no, rare, and frequent RtIs). Participants engaged in naturalistic NDRA, such as reading, phone use, and window gazing, during automated driving phases. The predictive HMI provided information about system limits via visual displays and auditory warnings, whereas the baseline group received warnings only seven seconds before limits. Results indicated that transition frequency significantly affected workload and acceptance, with higher frequencies leading to increased workload and reduced acceptance. Usability evaluations were also slightly impacted by transition frequency. However, trust in the system remained unaffected by these variables. The predictive HMI was utilized and accepted by participants, as evidenced by eye-tracking data and post-study questionnaires, but it failed to mitigate the negative effects of high transition frequencies on workload and acceptance. Regarding NDRA engagement, window gazing, chatting, and phone use were the most attractive activities. Descriptively, window gazing and chatting gained attractiveness with more frequent interruptions, while reading magazines and playing games were negatively affected by higher transition rates. The findings suggest that while predictive HMIs are valued by users, they are insufficient to counteract the discomfort and reduced acceptance associated with frequent automation disengagements in complex urban environments. The study highlights the importance of managing interruption frequency in HMI design for automated driving, particularly when users are engaged in diverse NDRA. The results imply that future HMI designs must address the specific needs of different activities, as some are more resilient to interruptions than others. This research contributes to the understanding of user behavior in automated driving, emphasizing the need for effective interruption management to enhance user comfort and system acceptance.

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Topics

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• automation
• temporal
• automation surprise
• acceptance adoption
• takeover transitions
• passenger motion sickness comfort

Information type

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• Empirical Findings: self report data, behavioral performance data
• Theoretical Contribution: conceptual framework